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Journal articles on the topic 'Collaborative and content based filtering'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Jalali, Meysam, Hojjat Gholizadeh, and Seyyed Alireza Hashemi Golpayegani. "An improved hybrid recommender system based on collaborative filtering, content based, and demographic filtering." International Journal of Academic Research 6, no. 6 (2014): 22–28. http://dx.doi.org/10.7813/2075-4124.2014/6-6/a.3.

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Wu, Xinyi. "Comparison Between Collaborative Filtering and Content-Based Filtering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 480–89. http://dx.doi.org/10.54097/hset.v16i.2627.

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With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by de

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3

Spoorthi, Chinivar. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. https://doi.org/10.35940/ijrte.C7274.0911322.

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<strong>Abstract:</strong> Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method

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Manmohan, Singh*1 &. Madhavi Shivhare2. "COMPARATIVE ANALYSIS ASSOCIATION RULE BASED COLLABORATIVE FILTERING." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 1 (2019): 211–19. https://doi.org/10.5281/zenodo.2553258.

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The World Wide Web have brought us an overabundant knowledge in varied fields and as a result of the data or information overloading, it is very arduous to find out related data. So, Recommendation System comes into existence. The main goal of this system is to recommend the best suitable items to the user or customer. The suggestions pertinent to decision making processes, like what things to obtain, which new music to listen to, which on-line latest news to search, or which image is best one from all. The advantages of recommendation system depend on efficiency of the system. The efficiency

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Necheporuk, Oleksandr, Svitlana Vashchenko, Nataliia Fedotova, Iryna Baranova, and Yaroslava Dehtiarenko. "ANALYSIS OF CONTENT RECOMMENDATION METHODS IN INFORMATION SERVICES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 3 (2024): 105–8. http://dx.doi.org/10.35784/iapgos.6203.

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The object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they operate on, thus prompting the utilisation of recommendation systems to maintain user engagement. Considering the different types of content, it is necessary to address the problem of data filtration based on their characteristics and user preferences. To solve this task, we analysed content-based and collaborative filtering methods using various

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Venkata, Bhanu Prasad Tolety, and Venkateswara Prasad Evani. "Hybrid content and collaborative filtering based recommendation system for e-learning platforms." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1543~1549. https://doi.org/10.11591/eei.v11i3.3861.

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Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of elearning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to gener

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B.Thorat, Poonam, R. M. Goudar, and Sunita Barve. "Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System." International Journal of Computer Applications 110, no. 4 (2015): 31–36. http://dx.doi.org/10.5120/19308-0760.

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Fu, Lei, and XiaoMing Ma. "An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering." Complexity 2021 (May 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589285.

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With the popularization of the Internet and the prevalence of online marketing, e-commerce systems provide enterprises with unlimited display space and provide customers with more product choices, while its structure is becoming increasingly complex. The emergence and application of the network marketing recommendation system have greatly improved this series of problems. It can effectively retain customers, prevent customer loss, and increase the cross-selling volume of the e-commerce system. However, the current network marketing recommendation system is still immature in practical applicati

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Yanisa Putri, Komang Sri, I Made Agus Dwi Suarjaya, and Wayan Oger Vihikan. "Sistem Rekomendasi Skincare Menggunakan Metode Content Based Filtering dan Collaborative Filtering." Decode: Jurnal Pendidikan Teknologi Informasi 4, no. 3 (2024): 764–74. http://dx.doi.org/10.51454/decode.v4i3.601.

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Seiring berjalannya waktu, setiap orang akan mengikuti perkembangan zaman yang semakin modern dalam segala hal terutama dalam hal perawatan kesehatan kulit wajah yang mengakibatkan kebutuhan akan produk perawatan kulit sangat diperlukan. Meningkatnya penggunaan teknologi digital dan maraknya berbagai macam jenis skincare membuat sistem rekomendasi produk skincare menjadi semakin penting. Sistem rekomendasi untuk pemilihan skincare ini dibuat untuk dapat merekomendasikan skincare yang cocok dengan tipe wajah pengguna serta kandungan yang sesuai dengan kulit wajah pengguna berdasarkan preferensi

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10

Nurjayanto, Bagus Wicaksono, and Z. K. A. Baizal. "NEWS RECOMMENDER SYSTEM USING HYBRID CONTENT-BASED FILTERING AND COLLABORATIVE FILTERING." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 9, no. 1 (2024): 26–33. http://dx.doi.org/10.29100/jipi.v9i1.4256.

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The development of online news services has offered users numerous choices, resulting in information overload. This makes it challenging for users to locate desired news within a spesific timeframe. to adress this, recommender systems have developed to help users discover and select news article.

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Kim, Kyung Soo, Doo Soo Chang, and Yong Suk Choi. "Boosting Memory-Based Collaborative Filtering Using Content-Metadata." Symmetry 11, no. 4 (2019): 561. http://dx.doi.org/10.3390/sym11040561.

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Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome th

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Raghuwanshi, S. K., and R. K. Pateriya. "Movie Recommendation System Content-Based and Collaborative Filtering." International Journal of Computer Sciences and Engineering 6, no. 4 (2018): 476–81. http://dx.doi.org/10.26438/ijcse/v6i4.476481.

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Mitroshyn, V. O., N. N. Shapovalova, I. O. Dotsenko, and N. H. Saitgareev. "The content personalization model based on collaborative filtering." Jornal of Kryvyi Rih National University, no. 52 (2021): 142–46. http://dx.doi.org/10.31721/2306-5451-2021-1-52-142-146.

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14

Harshal, Fulzele, Bhoite Mihir, Kanfade Prajwal, Yadav Ashutosh, Sahu Madhuri, and Thomas Achamma. "Movie Recommender System using Content Based andCollaborative Filtering." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1009–15. https://doi.org/10.5281/zenodo.7968788.

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Technology has evolved a lot from basic to advanced such as Machine learning, deep learning, Internet of things, Data Mining and many more. Recommender systems provide users with personalized suggestions for products or services also this system only rely on collaborative filtering. Movies are the source of Entertainment but finding the desired content is the problem. Aim of this paper is to improve the accuracy and performance of the regular filtering technique and also to recommend movies based on the content of the movie which users have watched earlier. Collaborative filtering recommends m

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15

Li, Lianhuan, Zheng Zhang, and Shaoda Zhang. "Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation." Scientific Programming 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/7427409.

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This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filteri

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PUTRA, KURNIA RAMADHAN, and MOHAMMAD ADITIYA RACHMAN. "Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu." MIND Journal 9, no. 2 (2024): 179–93. https://doi.org/10.26760/mindjournal.v9i2.179-193.

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AbstrakSistem rekomendasi dapat dimanfaatkan untuk membantu pengguna menemukan item atau informasi sesuai preferensi mereka, termasuk lagu. Metode seperti Collaborative Filtering (CF), Content-Based Filtering (CBF), dan Hybrid Filtering digunakan untuk meningkatkan kualitas rekomendasi berdasarkan interaksi pengguna dan karakteristik konten. Penelitian ini membandingkan efektivitas ketiga metode tersebut dalam rekomendasi lagu menggunakan dataset dengan 68.330 entri data. Metode CF dan CBF diterapkan secara terpisah, lalu dikombinasikan dalam pendekatan hybrid untuk mengevaluasi peningkatan ha

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17

Chinivar, Spoorthi. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. http://dx.doi.org/10.35940/ijrte.c7274.0911322.

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Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method for content-based filtering, a

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18

Juhi, Dhameliya, and Desai Nikita. "Job Recommendation System using Content and Collaborative Filtering Based Techniques." International Journal of Soft Computing and Engineering (IJSCE) 9, no. 3 (2019): 8–13. https://doi.org/10.35940/ijsce.C3266.099319.

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Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recomm

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19

Zhao, Feng, Fengwei Yan, Hai Jin, Laurence T. Yang, and Chen Yu. "Personalized Mobile Searching Approach Based on Combining Content-Based Filtering and Collaborative Filtering." IEEE Systems Journal 11, no. 1 (2017): 324–32. http://dx.doi.org/10.1109/jsyst.2015.2472996.

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20

Parasuraman, Desabandh, and Sathiyamoorthy Elumalai. "Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation." Journal of information and organizational sciences 45, no. 2 (2021): 535–51. http://dx.doi.org/10.31341/jios.45.2.10.

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Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering.

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21

Dr., Phan Thi Ha, and Thi Van Anh Trinh. "System based on Neighborhood-based Collaborative Filtering." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 8 (2022): 14–16. https://doi.org/10.35940/ijitee.H9126.0711822.

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<strong>Abstract</strong>: The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie

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22

Alsobhi, Aisha, and Ngiste Amare. "Ontology-Based Relational Product Recommendation System." Computational and Mathematical Methods in Medicine 2022 (September 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1591044.

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As online shopping has expanded, product recommendations on e-commerce websites have gained significance. Systems for recommending products use information about site navigation and user leave-over to suggest more products. Customers who use a product recommendation system choose better and find items more quickly. On e-commerce websites, collaborative and content-based filtering is used in product suggestion algorithms. Collaborative filtering is driven by user preference similarity and content-based filtering. While content-based filtering groups are related to products, collaboration groups

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23

Tolety, Venkata Bhanu Prasad, and Evani Venkateswara Prasad. "Hybrid content and collaborative filtering based recommendation system for e-learning platforms." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1543–49. http://dx.doi.org/10.11591/eei.v11i3.3861.

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Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of e-learning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to gene

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Goel, Shaurya. "Travel Recommendation System Using Content and Collaborative Filtering." Journal of Mechanical and Construction Engineering (JMCE) 4, no. 2 (2024): 1–8. https://doi.org/10.54060/a2zjournals.jmce.63.

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Tourism significantly impacts a nation's economy, yet there remains a void in platforms offering tailored information on local attractions. In our study, we propose a hybrid recommendation system amalgamating content and collaborative filtering methods to provide personalized tourist suggestions. This approach mitigates individual methods' drawbacks, enhancing recommendations' accuracy. To gauge item similarity, we employ cosine similarity while integrating SVD within a model-based collaborative filtering framework for improved outcomes. By utilizing a weighted hybridization technique, we effe

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Yan, Xuechao, Shuhan Qi, and Chang Chen. "Recommender Systems: Collaborative Filtering and Content-based Recommender System." Applied and Computational Engineering 2, no. 1 (2023): 346–51. http://dx.doi.org/10.54254/2755-2721/2/20220658.

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There are three algorithms of recommender systems proposed by this paper, which are item collaborative filtering(itemCF), user collaborative filtering(useCF) and content-based recommender system(CBRS). The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset. In accordance with the data we chose and ceaseless testing, we observe itemCF contains the most accurate rate. However, we theoretically and empirically conceive each algorithm owns different advantages and drawbacks, should be used in the specific circumst

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Ni, Wayan Priscila Yuni Praditya, Erna Permanasari Adhistya, Hidayah Indriana, Indana Zulfa Mulki, and Fauziati Silmi. "Collaborative and Content-Based Filtering Hybrid Method on Tourism Recommender System to Promote Less Explored Areas." International Journal of Applied Engineering & Technology 4, no. 2 (2022): 59–65. https://doi.org/10.5281/zenodo.7404490.

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The COVID-19 pandemic has significantly impacted various areas of life, including tourism. Currently, the tourism sector is starting to recover and start its activities. However, several tourist attractions have not been explored, thus making visitors less aware of information about these tours. This affects the number of tourist visits. Therefore, there is a need of an information technology approach to promote tourism objects, including a tourist recommendation system. This study proposed a hybrid recommendation system incorporating collaborative and content based filtering. This model is pr

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Su, Ja-Hwung, Wei-Yi Chang, and Vincent S. Tseng. "Effective social content-based collaborative filtering for music recommendation." Intelligent Data Analysis 21 (April 1, 2017): S195—S216. http://dx.doi.org/10.3233/ida-170878.

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Salter, J., and N. Antonopoulos. "CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering." IEEE Intelligent Systems 21, no. 1 (2006): 35–41. http://dx.doi.org/10.1109/mis.2006.4.

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Morzelona, Romi, and Sweta Batra. "Sentiment-aware Content Recommendation using LSTM-based Collaborative Filtering." Research Journal of Computer Systems and Engineering 3, no. 2 (2022): 32–38. http://dx.doi.org/10.52710/rjcse.53.

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The need for this work arises from the ever-growing demand for more precise and efficient content recommendation systems. Existing methods, while serving their purpose, are not without limitations. They often struggle to capture the intricate nuances of user sentiment, which is crucial for personalized content recommendations. This limitation results in suboptimal precision, accuracy, recall, and speed, ultimately leading to less satisfying user experiences. To address these shortcomings, this paper introduces a novel approach, leveraging the fusion of Bidirectional Long Short-Term Memory (BiL

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Qusai, Y. Shambour, M. Al-Zyoud Mahran, M. Al-Zyoud Mahran, and M. Kharma Qasem. "A doctor recommender system based on collaborative and content filtering." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2022): 884–93. https://doi.org/10.11591/ijece.v13i1.pp884-893.

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The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized ser

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Zhang, Shuting, Kechen Liu, Zekai Yu, Bowen Feng, and Zijie Ou. "Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction." Applied and Computational Engineering 2, no. 1 (2023): 927–39. http://dx.doi.org/10.54254/2755-2721/2/20220579.

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With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in t

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Neelima Jain and Dr. Abid Hussain. "Collaborative Filtering vs. Content-Based Filtering : A Machine Learning Perspective in Recommendation Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 918–27. https://doi.org/10.32628/cseit24102142.

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Recommendation systems have become fundamental components of modern digital platforms, powering personalized experiences across e-commerce, entertainment, and social media. This review paper provides a comprehensive analysis of the two primary recommendation approaches: collaborative filtering and content-based filtering, examined through a machine learning lens. We investigate their underlying algorithms, performance characteristics, applications, and emerging trends including deep learning implementations. Our analysis reveals that while collaborative filtering excels in discovering unexpect

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ZHOU, Tao, and Hua LI. "User context based collaborative filtering recommendation." Journal of Computer Applications 30, no. 4 (2010): 1076–78. http://dx.doi.org/10.3724/sp.j.1087.2010.01076.

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Phuong, Tu Minh, Do Thi Lien, and Nguyen Duy Phuong. "Graph-based context-aware collaborative filtering." Expert Systems with Applications 126 (July 2019): 9–19. http://dx.doi.org/10.1016/j.eswa.2019.02.015.

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Niemann, Katja, Maren Scheffel, Martin Friedrich, Uwe Kirschenmann, Hans-Christian Schmitz, and Martin Wolpers. "Usage-based Object Similarity." JUCS - Journal of Universal Computer Science 16, no. (16) (2010): 2272–90. https://doi.org/10.3217/jucs-016-16-2272.

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Recommender systems are widely used online to support users in finding relevant information. They can be based on different techniques such as content-based and collaborative filtering. In this paper, we introduce a new way of similarity calculation for item-based collaborative filtering. Thereby we focus on the usage of an object and not on the object's users as we claim the hypothesis that similarity of usage indicates content similarity. To prove this hypothesis we use learning objects accessible through the MACE portal where students can query several architectural repositories. For these

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BERKOVSKY, SHLOMO, YANIV EYTANI, and LARRY MANEVITZ. "EFFICIENT COLLABORATIVE FILTERING IN CONTENT-ADDRESSABLE SPACES." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 02 (2007): 265–89. http://dx.doi.org/10.1142/s0218001407005387.

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Collaborative Filtering (CF) is currently one of the most popular and most widely used personalization techniques. It generates personalized predictions based on the assumption that users with similar tastes prefer similar items. One of the major drawbacks of the CF from the computational point of view is its limited scalability since the computational effort required by the CF grows linearly both with the number of available users and items. This work proposes a novel efficient variant of the CF employed over a multidimensional content-addressable space. The proposed approach heuristically de

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Li, Zehang. "NCF-based Movie Recommendation System." Applied and Computational Engineering 104, no. 1 (2024): 72–77. http://dx.doi.org/10.54254/2755-2721/104/20241166.

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Abstract. This paper discusses the design and evaluation of a Neural Collaborative Filtering (NCF) model for movie recommendations using the MovieLens dataset. It addresses the limitations of traditional recommendation systems, such as content-based filtering and collaborative filtering, which struggle with data sparsity and the cold start problem. By incorporating deep learning, the NCF model enhances the accuracy and personalization of recommendations by learning the latent features of users and items and capturing complex interactions.

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Jaja, Visher Laja, Bambang Susanto, and Leopoldus Ricky Sasongko. "Penerapan Metode Item-Based Collaborative Filtering Untuk Sistem Rekomendasi Data MovieLens." d'CARTESIAN 9, no. 2 (2020): 78. http://dx.doi.org/10.35799/dc.9.2.2020.28274.

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Pada masa sekarang ini film telah menjadi salah satu hiburan favorit utama masyarakat. Jumlah film pertahun terhitung mencapai ribuan. Hal ini membuat penggemar film kesulitan dalam memilih film mana yang tepat untuk ditonton sesuai keinginan. Sehingga dibutuhkan sistem rekomendasi yang bertujuan untuk memberikan saran film mana yang akan dipilih. Sistem rekomendasi adalah sistem yang membantu pengguna dalam mengatasi informasi yang meluap dengan memberikan rekomendasi spesiﬁk bagi pengguna dan diharapkan rekomendasi tersebut bisa memenuhi keinginan dan kebutuhan pengguna. Terdapat tiga jenis

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Priskila, Ressa, M. Fajar, Septian Geges, and Widiatry Widiatry. "Penerapan Metode Collaborative Filtering dan Content Based Filtering Pada Sistem Rekomendasi Smartphone Android." Technologia : Jurnal Ilmiah 15, no. 3 (2024): 477. http://dx.doi.org/10.31602/tji.v15i3.15255.

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Masalah: Banyaknya variasi merk dan model smartphone Android yang beredar di pasaran khususnya di Indonesia dengan dengan berbagai pilihan fitur dan harga yang dapat dipilih. Hal ini, membuat konsumen kebingungan dalam menentukan smartphone android mana yang akan dibeli. Sehingga, konsumen akhirnya membeli sebuah Smartphone hanya karena tertarik dengan model atau tampilan fisik serta fasilitas terbaru tanpa menyesuaikan dengan kebutuhannya.Tujuan: Membangun sebuah Aplikasi Rekomendasi Smartphone Android berbasis Website menggunakan Collbarorative Based dan Content based filtering.Metode: Metod

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Martin Permana, Raihan, Asep Id Hadiana, and Puspita Nurul Sabrina. "Rekomendasi Pemilihan Sepeda Motor Menggunakan Metode Content Based Filtering Dan Item Based Colaborative Filtering." JURNAL TEKNIK INFORMATIKA UNIS 12, no. 2 (2024): 207–17. https://doi.org/10.33592/jutis.v12i2.5149.

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Sepeda motor menjadi andalan moda transportasi yang paling dibutuhkan orang tua dan remaja saat ini. Pemilihan sepeda motor sebagai alat transportasi yang paling diminati saat ini didasari oleh keunggulan sepeda motor itu sendiri dalam hal perawatan, bahan bakar, waktu tempuh yang lebih cepat, dan kemampuan mengatasi kemacetan yang terjadi di jalan kota. peneliti melakukan penggabungan dari kedua metode yang pertama yaitu content-based filtering dimana metode ini berdasarkan preferensi pengguna berdasarkan interaksi dengan data atau informasi selanjutnya akan dicocokkan dengan serangkaian kara

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Rupali, Hande1 Ajinkya Gutti* Kevin Shah2 Jeet Gandhi3 Vrushal Kamtikar4. "MOVIEMENDER- A MOVIE RECOMMENDER SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 11 (2016): 469–73. https://doi.org/10.5281/zenodo.167478.

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In today’s digital world where there is an endless variety of content to be consumed like books, videos, articles, movies, etc., finding the content of one’s liking has become an irksome task. On the other hand digital content providers want to engage as many users on their service as possible for the maximum time. This is where recommender system comes into picture where the content providers recommend users the content according to the users’ liking. In this paper we have proposed a movie recommender system MovieMender. The objective of MovieMender is to provide accurate movie recommendation

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Rao, P. Rama. "Movie Recommending System Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1034–38. http://dx.doi.org/10.22214/ijraset.2021.36377.

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Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of simil

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Ningrum, Aprilia Saptu Ningrum Saptu. "CONTENT BASED DAN COLLABORATIVE FILTERING PADA REKOMENDASI TUJUAN PARIWISATA DI DAERAH YOGYAKARTA." Telematika 16, no. 1 (2019): 44. http://dx.doi.org/10.31315/telematika.v16i1.3023.

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Abstract One of the famous cities for tourism is Yogyakarta. Yogyakarta has a variety of tourist destinations, staring from nature tourism, cultural and historical tourism, museums tour, beach tourism and special interest tours. both from domestic and foreign tourists. many tourist destinations in Yogyakarta, often makes tourists confused in choosing their destination. Based on these problem, then a system of recommendations is created that it can help tourists choose their destination. In research a system of recommendations for tourism destinations has been built in the Yogyakarta. This stud

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Manikantan, Aditya. "A Hybrid Recommendation System for Video Games: Combining Content-based & Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1647–53. http://dx.doi.org/10.22214/ijraset.2021.38246.

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Abstract: Recommending video games can be trickier than movies. When it comes to selecting a video game, many factors are involved such as its genre, platform on which it’s played, duration of main and side quests, and more. However, recommending games based on just these features won’t suffice as a person who, for example, enjoys a certain genre of game can equally enjoy a vastly different genre. Therefore, a scoring mechanism is required which takes into account both, features of a game (contentbased filtering) and also studies the buying patterns of people playing a particular game (collabo

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Li, Hongzhi, and Dezhi Han. "A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering." Mobile Information Systems 2020 (October 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/8896694.

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Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper,

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Pradana, Diyo Sukma, Prajoko Prajoko, and George Pri Hartawan. "Perbandingan Algoritma Content-Based Filtering dan Collaborative Filtering dalam Rekomendasi Kegiatan Ekstrakurikuler Siswa." Progresif: Jurnal Ilmiah Komputer 18, no. 2 (2022): 151. http://dx.doi.org/10.35889/progresif.v18i2.854.

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Geetha, G., M. Safa, C. Fancy, and D. Saranya. "A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System." Journal of Physics: Conference Series 1000 (April 2018): 012101. http://dx.doi.org/10.1088/1742-6596/1000/1/012101.

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Wu, Meng-Lun, Chia-Hui Chang, and Rui-Zhe Liu. "Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices." Expert Systems with Applications 41, no. 6 (2014): 2754–61. http://dx.doi.org/10.1016/j.eswa.2013.10.008.

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Widayanti, Riya. "Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering." Journal of Applied Data Sciences 4, no. 3 (2023): 289–302. http://dx.doi.org/10.47738/jads.v4i3.115.

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Journal, IJSREM. "Meal Map Pro." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27174.

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In this paper, we describe the recipe recommendation system in the culinary domain. Due to the widespread use of the internet, the whole world is connected, and different users from different countries share millions of recipes online, all over the world. As a result, users are unaware of all the recipes available on the internet. A recipe contains heterogeneous information’s such as ingredients, cooking process, categories, etc. Therefore, we believe that a recipe is an aggregation of these heterogeneous features. The majority of the recipe recommendation systems are based on content or colla

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